Problem1— Using ANN for Movie Genre Classification For this problem, you use the horror movies dataset you used in assignment 2 but instead of Naïve Bayes, you will use an ANN to classify movies into “thriller”, “no thriller” classes
movies = read.csv("C:/Users/CSC/Downloads/horror_movies.csv")
movies1- First use “qdap” package to remove stop words as follows (note: replace “movies” with whatever you called your dataframe)
# Assuming you have a dataframe named 'movies' containing the movie data
# Concatenate title, overview, and tagline into a new feature named 'Text'
movies$Text <- paste(movies$title, movies$overview, movies$tagline, sep = " ")
# View the first few rows of the updated dataframe
head(movies)NA#install.packages("qdap")
library(qdap)Loading required package: qdapDictionaries
Loading required package: qdapRegex
Loading required package: qdapTools
Registered S3 method overwritten by 'data.table':
method from
print.data.table
Loading required package: RColorBrewer
Attaching package: ‘qdap’
The following objects are masked from ‘package:base’:
Filter, proportionsmovies$Text=rm_stopwords(movies$Text, stopwords=tm::stopwords("english"),separate=FALSE, strip=TRUE)2- Randomize the order of rows, use the same seed as you did in assignment 2 so we can compare the two models on the same test dataset.
# did randomization
movies = movies[sample(nrow(movies)), ]
set.seed(1)3- Similar to assignment2, Convert thriller into a numeric variable with 0 representing none-thriller and 1 representing thriller.
# Converting thriller to numeric
movies$thriller = ifelse(grepl("Thriller", movies$genre_names, ignore.case = TRUE), 1,0)# Let's check our table for thriller conversion
table(movies$thriller)
0 1
24860 7680 str(movies)'data.frame': 32540 obs. of 22 variables:
$ id : int 747438 317957 970573 443377 22727 956351 516684 11124 599258 984640 ...
$ original_title : chr "Salvando a Papá" "最強女子コーセー伝説キョーコVSゆき" "チュンゲリア" "Amethyst" ...
$ title : chr "Saving Dad" "Kyoko vs. Yuki" "チュンゲリア" "Amethyst" ...
$ original_language: chr "es" "ja" "ja" "en" ...
$ overview : chr "After watching his favorite TV program, Medical Emergency, Alberto (8) asks he father, Félix (50) to play with "| __truncated__ "Two Japanese girls (one of them is a schoolgirl trained to be killer and another one is a lesbian) fighting wit"| __truncated__ "Those who become zombies due to saliva infection by Chu. The charm of this work, which is full of weakness in a"| __truncated__ "A girl tragically consumes a full vial of LSD and quickly succumbs to a bizarre fantasy world, with strange bei"| __truncated__ ...
$ tagline : chr "Alberto runs a surgery on his dad, by following step by step his favorite TV show." "" "" "She took too much." ...
$ release_date : chr "2020-11-19" "2000-03-03" "2015-05-02" "2016-03-09" ...
$ poster_path : chr "/imN9IuchtuKBpE06054sHSm3sqK.jpg" "/rokFU7yZ7oLEdaa3IHa5oADJavx.jpg" "/g45Qln2cTF1qXw7O4IvAulpqFM4.jpg" NA ...
$ popularity : num 0.6 0.6 0.6 1.2 4.68 ...
$ vote_count : int 0 3 0 0 68 1 8 1 1 0 ...
$ vote_average : num 0 3.3 0 0 2.1 2 6.1 7 6 0 ...
$ budget : int 1000 0 0 0 0 0 0 0 0 0 ...
$ revenue : int 0 0 0 0 0 0 0 0 0 0 ...
$ runtime : int 7 60 37 70 54 76 5 5 7 115 ...
$ status : chr "Released" "Released" "Released" "Released" ...
$ adult : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
$ backdrop_path : chr "/9PjA9wO4nomOgW1hQGFPec6XZKm.jpg" "/notveoCCNiHhXffVF32k9NMQdfL.jpg" NA NA ...
$ genre_names : chr "Horror" "Action, Drama, Horror" "Horror" "Adventure, Fantasy, Horror" ...
$ collection : int NA NA NA NA NA NA NA NA NA NA ...
$ collection_name : chr NA NA NA NA ...
$ Text : chr "saving dad watching favorite tv program medical emergency alberto asks father félix play alberto trying ask bug"| __truncated__ "kyoko vs yuki two japanese girls one schoolgirl trained killer another one lesbian fighting incredibly bloody results" "チュンゲリア become zombies due saliva infection chu charm work full weakness good sense titles associated sang"| __truncated__ "amethyst girl tragically consumes full vial lsd quickly succumbs bizarre fantasy world strange beings magical o"| __truncated__ ...
$ thriller : num 0 0 0 0 0 0 0 1 0 1 ...4- Split the data three ways in to train/validation/ and test sets. Use 65% of data for training, 15% for validation and 20% for testing. Make sure that you are using the same test set as you used in assignment 2 so you can compare the ANN model with your naïve Bayes model in assignmentt2.
#Important thing to note is: We used 80% data for training and 20% for testing for our Assignment 2 using Naive bayes. Therefore to have same comparisons, we’ll divide the data just the same way and later from the 80% data of training, we’ll split it 65% for training and 15% for validation.
# Let's split the data
n = nrow(movies)
train_index = sample(1:n, 0.8*n)
movies_train = movies[train_index, ]
movies_test = movies[-train_index,-22]
test_labels = movies[-train_index,22 ]
n_val = nrow(movies_train)
val_index = sample(1:n_val, 0.65*n_val)
movies_training = movies_train[val_index,-22 ]
movies_validation = movies_train[-val_index, -22]
train_labels = movies_train[val_index, 22 ]
val_labels = movies_train[-val_index,22 ]# Checking for tables
table(train_labels)train_labels
0 1
12927 3993 table(test_labels)test_labels
0 1
5015 1493 table(val_labels)val_labels
0 1
6918 2194 5- Keras has a preprocessing layer, called layer_text_vectorization, this layer creates a document- term matrix where rows represent movie texts and columns represent terms. Use the following code segment to create document-term matrix for your training, validation and test datasets you created above. (Note: replace train, test, and val with the names you gave to your train, test and validation sets):
library(keras)
text_vectorizer = layer_text_vectorization(output_mode = "tf_idf", ngrams = 2, max_tokens = 5000)2024-04-10 21:57:15.234015: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: SSE SSE2 SSE3 SSE4.1 SSE4.2 AVX AVX2 AVX512F FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.text_vectorizer %>% adapt(movies_training$Text)
train_dtm = text_vectorizer(movies_training$Text)2024-04-10 21:57:18.789275: W tensorflow/tsl/framework/cpu_allocator_impl.cc:83] Allocation of 338400000 exceeds 10% of free system memory.
2024-04-10 21:57:18.866970: W tensorflow/tsl/framework/cpu_allocator_impl.cc:83] Allocation of 338400000 exceeds 10% of free system memory.val_dtm = text_vectorizer(movies_validation$Text)2024-04-10 21:57:19.321350: W tensorflow/tsl/framework/cpu_allocator_impl.cc:83] Allocation of 182240000 exceeds 10% of free system memory.
2024-04-10 21:57:19.359811: W tensorflow/tsl/framework/cpu_allocator_impl.cc:83] Allocation of 182240000 exceeds 10% of free system memory.test_dtm = text_vectorizer(movies_test$Text)2024-04-10 21:57:19.878395: W tensorflow/tsl/framework/cpu_allocator_impl.cc:83] Allocation of 130160000 exceeds 10% of free system memory. Q1. (5 pts) Create an ANN model with two hidden layers to classify movies into two classes (“Thriller” and “no-thriller”). Note: This is a binary classification problem so make sure that you are using a correct loss function as well correct number of neurons/units in the final/output layer with correct activation function. Once the model has completed its training, get the prediction for the test data as follows.: Use a cross table to compare these predicted labels to the true labels in the test data and interpret the table.
train_dtm = scale(train_dtm)
col_means_train = attr(train_dtm, "scaled:center")
col_stddevs_train = attr(train_dtm, "scaled:scale")
test_dtm = scale(test_dtm, center = col_means_train, scale = col_stddevs_train)
val_dtm = scale(val_dtm, center = col_means_train, scale = col_stddevs_train)
model <- keras_model_sequential() %>%
layer_dense(units = 32, activation = "relu", input_shape = dim(train_dtm)[2]) %>%
layer_dense(units = 2, activation = "relu") %>%
layer_dense(units = 1, activation = "sigmoid")
model %>% compile(
loss = "binary_crossentropy",
optimizer = "adam",
metrics = c("accuracy")
)
modelModel: "sequential"
_________________________________________________________________________________________________________________
Layer (type) Output Shape Param #
=================================================================================================================
dense_2 (Dense) (None, 32) 160032
dense_1 (Dense) (None, 2) 66
dense (Dense) (None, 1) 3
=================================================================================================================
Total params: 160101 (625.39 KB)
Trainable params: 160101 (625.39 KB)
Non-trainable params: 0 (0.00 Byte)
_________________________________________________________________________________________________________________set.seed(1)
history <- model %>% fit(
train_dtm, train_labels,
epochs = 20,
batch_size = 50,
validation_data = list(val_dtm, val_labels), verbose = 2
)Epoch 1/20
339/339 - 6s - loss: 0.5811 - accuracy: 0.7352 - val_loss: 0.5363 - val_accuracy: 0.7592 - 6s/epoch - 18ms/step
Epoch 2/20
339/339 - 2s - loss: 0.4546 - accuracy: 0.7650 - val_loss: 0.5494 - val_accuracy: 0.7572 - 2s/epoch - 6ms/step
Epoch 3/20
339/339 - 2s - loss: 0.3521 - accuracy: 0.8105 - val_loss: 0.6700 - val_accuracy: 0.7317 - 2s/epoch - 5ms/step
Epoch 4/20
339/339 - 2s - loss: 0.2487 - accuracy: 0.9017 - val_loss: 0.8998 - val_accuracy: 0.7129 - 2s/epoch - 6ms/step
Epoch 5/20
339/339 - 2s - loss: 0.1561 - accuracy: 0.9395 - val_loss: 1.2145 - val_accuracy: 0.6986 - 2s/epoch - 6ms/step
Epoch 6/20
339/339 - 2s - loss: 0.0909 - accuracy: 0.9671 - val_loss: 1.5757 - val_accuracy: 0.6957 - 2s/epoch - 6ms/step
Epoch 7/20
339/339 - 2s - loss: 0.0536 - accuracy: 0.9810 - val_loss: 1.8818 - val_accuracy: 0.7006 - 2s/epoch - 5ms/step
Epoch 8/20
339/339 - 2s - loss: 0.0356 - accuracy: 0.9875 - val_loss: 2.1290 - val_accuracy: 0.6889 - 2s/epoch - 5ms/step
Epoch 9/20
339/339 - 2s - loss: 0.0259 - accuracy: 0.9906 - val_loss: 2.3658 - val_accuracy: 0.6941 - 2s/epoch - 5ms/step
Epoch 10/20
339/339 - 2s - loss: 0.0222 - accuracy: 0.9912 - val_loss: 2.5218 - val_accuracy: 0.6918 - 2s/epoch - 5ms/step
Epoch 11/20
339/339 - 2s - loss: 0.0199 - accuracy: 0.9923 - val_loss: 2.6485 - val_accuracy: 0.6925 - 2s/epoch - 5ms/step
Epoch 12/20
339/339 - 2s - loss: 0.0195 - accuracy: 0.9929 - val_loss: 2.7497 - val_accuracy: 0.6847 - 2s/epoch - 5ms/step
Epoch 13/20
339/339 - 2s - loss: 0.0193 - accuracy: 0.9924 - val_loss: 2.7927 - val_accuracy: 0.6906 - 2s/epoch - 5ms/step
Epoch 14/20
339/339 - 2s - loss: 0.0188 - accuracy: 0.9928 - val_loss: 2.8592 - val_accuracy: 0.6892 - 2s/epoch - 6ms/step
Epoch 15/20
339/339 - 2s - loss: 0.0183 - accuracy: 0.9927 - val_loss: 2.9143 - val_accuracy: 0.6876 - 2s/epoch - 5ms/step
Epoch 16/20
339/339 - 2s - loss: 0.0179 - accuracy: 0.9927 - val_loss: 2.9314 - val_accuracy: 0.6894 - 2s/epoch - 5ms/step
Epoch 17/20
339/339 - 2s - loss: 0.0178 - accuracy: 0.9924 - val_loss: 2.9544 - val_accuracy: 0.6947 - 2s/epoch - 5ms/step
Epoch 18/20
339/339 - 2s - loss: 0.0171 - accuracy: 0.9928 - val_loss: 3.0121 - val_accuracy: 0.6847 - 2s/epoch - 5ms/step
Epoch 19/20
339/339 - 2s - loss: 0.0187 - accuracy: 0.9928 - val_loss: 2.9877 - val_accuracy: 0.6945 - 2s/epoch - 5ms/step
Epoch 20/20
339/339 - 2s - loss: 0.0168 - accuracy: 0.9931 - val_loss: 3.0653 - val_accuracy: 0.6948 - 2s/epoch - 5ms/stepprint(history)
Final epoch (plot to see history):
loss: 0.01679
accuracy: 0.9931
val_loss: 3.065
val_accuracy: 0.6948 # Let's predict our model on test_dtm
predictions = model %>% predict(test_dtm)
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204/204 [==============================] - 1s 6ms/step
204/204 [==============================] - 1s 6ms/steppred_labels = ifelse(predictions > 0.5,1,0)
# Creating a cross table to compare the predicted labels to the true labels
movies_table = table(pred_labels, test_labels)
movies_table test_labels
pred_labels 0 1
0 4114 1038
1 901 455#As per my interpretation for this, we predicted around (4698+185) labels correctly as they are supposed to be predicted and around (1310+315) are negative.
Let’s calculate it’s accuracy
movies_pred_accuracy = sum(diag(movies_table)) / sum(movies_table)
print(paste("The accuracy is:", movies_pred_accuracy*100, "%"))[1] "The accuracy is: 70.2059004302397 %"Q2. (5 pts) Use “tfruns” package to tune your ANN’s hyper- parameters including the number of nodes in each hidden layer, the batch_size, and learning_rate). Validate each model on the validation set. Answer the following questions: 1- (2pt) Which model ( which hyper-parameter combination) resulted in the best accuracy on the validation data? Make sure that you print the returned value from tfruns and report the run with the highest validation accuracy. Note: the best run is not necessarily the first run. Print the entire table returned by tfruns to see which run has the highest validation accuracy and that would be your best run. 2- (2pt) take a screenshot of the learning curves of your best model and save it. Does your best model overfit? 3- (1pt) Does your validation_loss stop decreasing after several epochs? If so, at roughly which epoch does your validation_loss stop decreasing?
#install.packages("tfruns")
library(tfruns)
runs = tuning_run("C:/Users/CSC/Desktop/ML/ML-Assignment 4.R",
flags = list(
nodes = c(32,64,128,392),
learning_rate = c(0.001, 0.01, 0.05, 0.1),
batch_size = c(16, 32, 64, 128),
activation = c("relu","sigmoid", "tanh")
),
sample = 0.03)192 total combinations of flags
(sampled to 6 combinations)yTraining run 1/6 (flags = list(392, 0.001, 16, "sigmoid"))
Using run directory runs/2024-04-11T02-58-58Z
> flag = flags(
+ flag_numeric("nodes", 128),
+ flag_numeric("batch_size", 32),
+ flag_string("activation", "relu"),
+ flag_numeric("learning_ ..." ... [TRUNCATED]
> callbacks = list(callback_early_stopping(monitor="val_loss", patience = 5, restore_best_weights = TRUE))
> model = keras_model_sequential()
> model %>%
+ layer_flatten(input_shape = dim(train_dtm)[2]) %>%
+ layer_dense(flag$nodes, activation = flag$activation) %>%
+ layer_dense(units .... [TRUNCATED]
> model %>% compile(
+ optimizer = optimizer_adam(learning_rate= flag$learning_rate),
+ loss = 'binary_crossentropy',
+ metrics = c('accuracy')
.... [TRUNCATED]
> model %>% fit(
+ train_dtm, train_labels,
+ epochs = 30,
+ batch_size = flag$batch_size,
+ validation_data = list(test_dtm, test_labels),
+ .... [TRUNCATED]
Epoch 1/30
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1058/1058 [==============================] - 35s 32ms/step - loss: 0.5883 - accuracy: 0.7415
1058/1058 [==============================] - 37s 34ms/step - loss: 0.5883 - accuracy: 0.7415 - val_loss: 0.5145 - val_accuracy: 0.7706
Epoch 2/30
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1058/1058 [==============================] - 39s 37ms/step - loss: 0.4439 - accuracy: 0.7950
1058/1058 [==============================] - 41s 39ms/step - loss: 0.4439 - accuracy: 0.7950 - val_loss: 0.5285 - val_accuracy: 0.7583
Epoch 3/30
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1058/1058 [==============================] - 38s 36ms/step - loss: 0.3620 - accuracy: 0.8431
1058/1058 [==============================] - 40s 38ms/step - loss: 0.3620 - accuracy: 0.8431 - val_loss: 0.6136 - val_accuracy: 0.7087
Epoch 4/30
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1058/1058 [==============================] - 37s 35ms/step - loss: 0.2654 - accuracy: 0.8959
1058/1058 [==============================] - 38s 36ms/step - loss: 0.2654 - accuracy: 0.8959 - val_loss: 0.7709 - val_accuracy: 0.7025
Epoch 5/30
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1058/1058 [==============================] - 36s 34ms/step - loss: 0.1652 - accuracy: 0.9440
1058/1058 [==============================] - 37s 35ms/step - loss: 0.1652 - accuracy: 0.9440 - val_loss: 0.9703 - val_accuracy: 0.7074
Epoch 6/30
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1058/1058 [==============================] - 37s 35ms/step - loss: 0.0889 - accuracy: 0.9736
1058/1058 [==============================] - 39s 37ms/step - loss: 0.0889 - accuracy: 0.9736 - val_loss: 1.1802 - val_accuracy: 0.6982
Run completed: runs/2024-04-11T02-58-58Z
Training run 2/6 (flags = list(128, 0.01, 64, "tanh"))
Using run directory runs/2024-04-11T03-03-04Z
> flag = flags(
+ flag_numeric("nodes", 128),
+ flag_numeric("batch_size", 32),
+ flag_string("activation", "relu"),
+ flag_numeric("learning_ ..." ... [TRUNCATED]
> callbacks = list(callback_early_stopping(monitor="val_loss", patience = 5, restore_best_weights = TRUE))
> model = keras_model_sequential()
> model %>%
+ layer_flatten(input_shape = dim(train_dtm)[2]) %>%
+ layer_dense(flag$nodes, activation = flag$activation) %>%
+ layer_dense(units .... [TRUNCATED]
> model %>% compile(
+ optimizer = optimizer_adam(learning_rate= flag$learning_rate),
+ loss = 'binary_crossentropy',
+ metrics = c('accuracy')
.... [TRUNCATED]
> model %>% fit(
+ train_dtm, train_labels,
+ epochs = 30,
+ batch_size = flag$batch_size,
+ validation_data = list(test_dtm, test_labels),
+ .... [TRUNCATED]
Epoch 1/30
1/265 [..............................] - ETA: 14:41 - loss: 0.9366 - accuracy: 0.4062
5/265 [..............................] - ETA: 3s - loss: 2.5056 - accuracy: 0.5594 WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0126s vs `on_train_batch_end` time: 0.0283s). Check your callbacks.
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265/265 [==============================] - 8s 17ms/step - loss: 0.6838 - accuracy: 0.7163
265/265 [==============================] - 9s 23ms/step - loss: 0.6838 - accuracy: 0.7163 - val_loss: 0.5320 - val_accuracy: 0.7624
Epoch 2/30
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265/265 [==============================] - 6s 22ms/step - loss: 0.4634 - accuracy: 0.7862
265/265 [==============================] - 6s 24ms/step - loss: 0.4634 - accuracy: 0.7862 - val_loss: 0.5440 - val_accuracy: 0.7492
Epoch 3/30
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265/265 [==============================] - 4s 16ms/step - loss: 0.4080 - accuracy: 0.8160
265/265 [==============================] - 5s 18ms/step - loss: 0.4080 - accuracy: 0.8160 - val_loss: 0.5834 - val_accuracy: 0.7345
Epoch 4/30
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265/265 [==============================] - 4s 16ms/step - loss: 0.3650 - accuracy: 0.8417
265/265 [==============================] - 5s 18ms/step - loss: 0.3650 - accuracy: 0.8417 - val_loss: 0.6736 - val_accuracy: 0.7056
Epoch 5/30
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265/265 [==============================] - 5s 17ms/step - loss: 0.3303 - accuracy: 0.8605
265/265 [==============================] - 5s 20ms/step - loss: 0.3303 - accuracy: 0.8605 - val_loss: 0.6945 - val_accuracy: 0.7160
Epoch 6/30
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265/265 [==============================] - 5s 18ms/step - loss: 0.2956 - accuracy: 0.8790 - val_loss: 0.7721 - val_accuracy: 0.7030
Run completed: runs/2024-04-11T03-03-04Z
Training run 3/6 (flags = list(32, 0.001, 16, "tanh"))
Using run directory runs/2024-04-11T03-03-53Z
> flag = flags(
+ flag_numeric("nodes", 128),
+ flag_numeric("batch_size", 32),
+ flag_string("activation", "relu"),
+ flag_numeric("learning_ ..." ... [TRUNCATED]
> callbacks = list(callback_early_stopping(monitor="val_loss", patience = 5, restore_best_weights = TRUE))
> model = keras_model_sequential()
> model %>%
+ layer_flatten(input_shape = dim(train_dtm)[2]) %>%
+ layer_dense(flag$nodes, activation = flag$activation) %>%
+ layer_dense(units .... [TRUNCATED]
> model %>% compile(
+ optimizer = optimizer_adam(learning_rate= flag$learning_rate),
+ loss = 'binary_crossentropy',
+ metrics = c('accuracy')
.... [TRUNCATED]
> model %>% fit(
+ train_dtm, train_labels,
+ epochs = 30,
+ batch_size = flag$batch_size,
+ validation_data = list(test_dtm, test_labels),
+ .... [TRUNCATED]
Epoch 1/30
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1058/1058 [==============================] - 7s 6ms/step - loss: 0.6963 - accuracy: 0.6720
1058/1058 [==============================] - 13s 11ms/step - loss: 0.6963 - accuracy: 0.6720 - val_loss: 0.5319 - val_accuracy: 0.7621
Epoch 2/30
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1058/1058 [==============================] - 5s 5ms/step - loss: 0.4474 - accuracy: 0.8036
1058/1058 [==============================] - 7s 6ms/step - loss: 0.4474 - accuracy: 0.8036 - val_loss: 0.5419 - val_accuracy: 0.7485
Epoch 3/30
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1058/1058 [==============================] - 5s 5ms/step - loss: 0.3149 - accuracy: 0.8712
1058/1058 [==============================] - 6s 6ms/step - loss: 0.3149 - accuracy: 0.8712 - val_loss: 0.6472 - val_accuracy: 0.7277
Epoch 4/30
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1058/1058 [==============================] - 5s 5ms/step - loss: 0.2066 - accuracy: 0.9249
1058/1058 [==============================] - 6s 6ms/step - loss: 0.2066 - accuracy: 0.9249 - val_loss: 0.7769 - val_accuracy: 0.7153
Epoch 5/30
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Epoch 6/30
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1058/1058 [==============================] - 5s 5ms/step - loss: 0.0856 - accuracy: 0.9725 - val_loss: 0.9946 - val_accuracy: 0.7027
Run completed: runs/2024-04-11T03-03-53Z
Training run 4/6 (flags = list(64, 0.001, 64, "tanh"))
Using run directory runs/2024-04-11T03-04-48Z
> flag = flags(
+ flag_numeric("nodes", 128),
+ flag_numeric("batch_size", 32),
+ flag_string("activation", "relu"),
+ flag_numeric("learning_ ..." ... [TRUNCATED]
> callbacks = list(callback_early_stopping(monitor="val_loss", patience = 5, restore_best_weights = TRUE))
> model = keras_model_sequential()
> model %>%
+ layer_flatten(input_shape = dim(train_dtm)[2]) %>%
+ layer_dense(flag$nodes, activation = flag$activation) %>%
+ layer_dense(units .... [TRUNCATED]
> model %>% compile(
+ optimizer = optimizer_adam(learning_rate= flag$learning_rate),
+ loss = 'binary_crossentropy',
+ metrics = c('accuracy')
.... [TRUNCATED]
> model %>% fit(
+ train_dtm, train_labels,
+ epochs = 30,
+ batch_size = flag$batch_size,
+ validation_data = list(test_dtm, test_labels),
+ .... [TRUNCATED]
Epoch 1/30
1/265 [..............................] - ETA: 5:40 - loss: 0.8423 - accuracy: 0.3750WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0080s vs `on_train_batch_end` time: 0.0242s). Check your callbacks.
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265/265 [==============================] - 5s 13ms/step - loss: 0.8563 - accuracy: 0.6178
265/265 [==============================] - 6s 19ms/step - loss: 0.8563 - accuracy: 0.6178 - val_loss: 0.5699 - val_accuracy: 0.7031
Epoch 2/30
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265/265 [==============================] - 3s 12ms/step - loss: 0.4322 - accuracy: 0.8187
265/265 [==============================] - 4s 14ms/step - loss: 0.4322 - accuracy: 0.8187 - val_loss: 0.5890 - val_accuracy: 0.7207
Epoch 3/30
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265/265 [==============================] - 3s 11ms/step - loss: 0.2935 - accuracy: 0.8894
265/265 [==============================] - 4s 13ms/step - loss: 0.2935 - accuracy: 0.8894 - val_loss: 0.6638 - val_accuracy: 0.7116
Epoch 4/30
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265/265 [==============================] - 3s 12ms/step - loss: 0.1809 - accuracy: 0.9436
265/265 [==============================] - 4s 14ms/step - loss: 0.1809 - accuracy: 0.9436 - val_loss: 0.7711 - val_accuracy: 0.7081
Epoch 5/30
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265/265 [==============================] - 3s 10ms/step - loss: 0.0995 - accuracy: 0.9749
265/265 [==============================] - 3s 12ms/step - loss: 0.0995 - accuracy: 0.9749 - val_loss: 0.8653 - val_accuracy: 0.7065
Epoch 6/30
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265/265 [==============================] - 3s 11ms/step - loss: 0.0565 - accuracy: 0.9877
265/265 [==============================] - 3s 13ms/step - loss: 0.0565 - accuracy: 0.9877 - val_loss: 0.9346 - val_accuracy: 0.7016
Run completed: runs/2024-04-11T03-04-48Z
Training run 5/6 (flags = list(128, 0.05, 64, "relu"))
Using run directory runs/2024-04-11T03-05-27Z
> flag = flags(
+ flag_numeric("nodes", 128),
+ flag_numeric("batch_size", 32),
+ flag_string("activation", "relu"),
+ flag_numeric("learning_ ..." ... [TRUNCATED]
> callbacks = list(callback_early_stopping(monitor="val_loss", patience = 5, restore_best_weights = TRUE))
> model = keras_model_sequential()
> model %>%
+ layer_flatten(input_shape = dim(train_dtm)[2]) %>%
+ layer_dense(flag$nodes, activation = flag$activation) %>%
+ layer_dense(units .... [TRUNCATED]
> model %>% compile(
+ optimizer = optimizer_adam(learning_rate= flag$learning_rate),
+ loss = 'binary_crossentropy',
+ metrics = c('accuracy')
.... [TRUNCATED]
> model %>% fit(
+ train_dtm, train_labels,
+ epochs = 30,
+ batch_size = flag$batch_size,
+ validation_data = list(test_dtm, test_labels),
+ .... [TRUNCATED]
Epoch 1/30
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265/265 [==============================] - 6s 18ms/step - loss: 9.6192 - accuracy: 0.6838
265/265 [==============================] - 10s 31ms/step - loss: 9.6192 - accuracy: 0.6838 - val_loss: 8.6488 - val_accuracy: 0.5748
Epoch 2/30
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Epoch 3/30
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265/265 [==============================] - 4s 17ms/step - loss: 9.7129 - accuracy: 0.7241
265/265 [==============================] - 5s 19ms/step - loss: 9.7129 - accuracy: 0.7241 - val_loss: 18.8434 - val_accuracy: 0.6069
Epoch 4/30
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265/265 [==============================] - 5s 17ms/step - loss: 13.2782 - accuracy: 0.7407
265/265 [==============================] - 5s 19ms/step - loss: 13.2782 - accuracy: 0.7407 - val_loss: 28.4861 - val_accuracy: 0.5676
Epoch 5/30
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265/265 [==============================] - 4s 17ms/step - loss: 17.4223 - accuracy: 0.7581
265/265 [==============================] - 5s 19ms/step - loss: 17.4223 - accuracy: 0.7581 - val_loss: 25.9761 - val_accuracy: 0.6882
Epoch 6/30
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265/265 [==============================] - 4s 16ms/step - loss: 11.1823 - accuracy: 0.8106
265/265 [==============================] - 5s 18ms/step - loss: 11.1823 - accuracy: 0.8106 - val_loss: 45.7461 - val_accuracy: 0.6825
Epoch 7/30
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265/265 [==============================] - 5s 19ms/step - loss: 10.2327 - accuracy: 0.8383 - val_loss: 61.9448 - val_accuracy: 0.6693
Run completed: runs/2024-04-11T03-05-27Z
Training run 6/6 (flags = list(64, 0.1, 16, "relu"))
Using run directory runs/2024-04-11T03-06-14Z
> flag = flags(
+ flag_numeric("nodes", 128),
+ flag_numeric("batch_size", 32),
+ flag_string("activation", "relu"),
+ flag_numeric("learning_ ..." ... [TRUNCATED]
> callbacks = list(callback_early_stopping(monitor="val_loss", patience = 5, restore_best_weights = TRUE))
> model = keras_model_sequential()
> model %>%
+ layer_flatten(input_shape = dim(train_dtm)[2]) %>%
+ layer_dense(flag$nodes, activation = flag$activation) %>%
+ layer_dense(units .... [TRUNCATED]
> model %>% compile(
+ optimizer = optimizer_adam(learning_rate= flag$learning_rate),
+ loss = 'binary_crossentropy',
+ metrics = c('accuracy')
.... [TRUNCATED]
> model %>% fit(
+ train_dtm, train_labels,
+ epochs = 30,
+ batch_size = flag$batch_size,
+ validation_data = list(test_dtm, test_labels),
+ .... [TRUNCATED]
Epoch 1/30
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1058/1058 [==============================] - 16s 10ms/step - loss: 238.9497 - accuracy: 0.6850
1058/1058 [==============================] - 18s 12ms/step - loss: 238.9497 - accuracy: 0.6850 - val_loss: 189.3791 - val_accuracy: 0.7423
Epoch 2/30
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1058/1058 [==============================] - 10s 9ms/step - loss: 134.1926 - accuracy: 0.6991
1058/1058 [==============================] - 11s 10ms/step - loss: 134.1926 - accuracy: 0.6991 - val_loss: 75.0237 - val_accuracy: 0.6260
Epoch 3/30
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1058/1058 [==============================] - 9s 9ms/step - loss: 129.6601 - accuracy: 0.7240
1058/1058 [==============================] - 10s 10ms/step - loss: 129.6601 - accuracy: 0.7240 - val_loss: 242.3461 - val_accuracy: 0.7420
Epoch 4/30
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1058/1058 [==============================] - 9s 8ms/step - loss: 117.0088 - accuracy: 0.7393
1058/1058 [==============================] - 10s 10ms/step - loss: 117.0088 - accuracy: 0.7393 - val_loss: 209.3943 - val_accuracy: 0.6581
Epoch 5/30
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1058/1058 [==============================] - 9s 8ms/step - loss: 114.8546 - accuracy: 0.7647
1058/1058 [==============================] - 10s 9ms/step - loss: 114.8546 - accuracy: 0.7647 - val_loss: 622.3302 - val_accuracy: 0.6773
Epoch 6/30
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Epoch 7/30
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Run completed: runs/2024-04-11T03-06-14Zruns = runs[order(runs$metric_val_loss), ]
runsData frame: 6 x 24 # ... with 19 more columns:
# flag_nodes, flag_batch_size, flag_activation, flag_learning_rate, epochs, epochs_completed, metrics,
# model, loss_function, optimizer, learning_rate, script, start, end, completed, output, source_code,
# context, type#As per this run, The best metric_val_accuracy is 0.7299 having metric_val_loss as 0.6097.
view_run(runs$run_dir[1])Warning: argument 'compressed' is ignored for the internal methodWarning: incomplete final line found on 'C:\Users\CSC\AppData\Local\Temp\Rtmp2fJeNs\file1c085a561cf9/source/ML-Assignment 4.R'#After viewing the learning curve, I can deduce that: # 1. Our validation accuracy and loss are respectively: 71% and 0.60. # 2. Our batch_size and nodes should be 64 # 3.Activation layer and loss is sigmoid and cross_entropy respectively. # 4. The learning rate is 0.05
Q3. (5 pts)Now that we tuned the hyperparameters and selected the best model, we don’t need to withhold validation data anymore and can use it for training. Add the validation data to the train data. You can do this by first, converting your train_dtm and val_dtm into matrices and then combining them using “rbind”. Make sure that you also combine the train and validation labels . Now re-train your best model on this new training data and evaluate it on the test data. Compute precision and recall for the Thriller movies. How does this model perform compare to your naïve Bayes model in assignment 2
train_dtm = as.matrix(train_dtm)
val_dtm = as.matrix(val_dtm)
combined_dtm = rbind(train_dtm, val_dtm)
combined_labels = c(train_labels, val_labels)# Retraining the data:
model <- keras_model_sequential() %>%
layer_dense(units = 64, activation = "relu", input_shape = dim(combined_dtm)[2]) %>%
layer_dense(units = 16, activation = "sigmoid")
layer_dense(units = 1, activation = "sigmoid")<keras.src.layers.core.dense.Dense object at 0x000001762D4C2E90>
model %>% compile(
loss = "binary_crossentropy",
optimizer = "adam",
metrics = c("accuracy")
)
modelModel: "sequential"
_________________________________________________________________________________________________________________
Layer (type) Output Shape Param #
=================================================================================================================
dense_1 (Dense) (None, 64) 320064
dense (Dense) (None, 16) 1040
=================================================================================================================
Total params: 321104 (1.22 MB)
Trainable params: 321104 (1.22 MB)
Non-trainable params: 0 (0.00 Byte)
_________________________________________________________________________________________________________________set.seed(1)
history <- model %>% fit(
combined_dtm, combined_labels,
epochs = 20,
batch_size = 64,
validation_data = list(test_dtm, test_labels)
)Epoch 1/20Error in py_call_impl(callable, call_args$unnamed, call_args$named) :
ValueError: in user code:
<...truncated...>y_p, sample_weight=sw)
File "C:\Users\CSC\DOCUME~1\VIRTUA~1\R-TENS~1\Lib\site-packages\keras\src\losses.py", line 142, in __call__
losses = call_fn(y_true, y_pred)
File "C:\Users\CSC\DOCUME~1\VIRTUA~1\R-TENS~1\Lib\site-packages\keras\src\losses.py", line 268, in call **
return ag_fn(y_true, y_pred, **self._fn_kwargs)
File "C:\Users\CSC\DOCUME~1\VIRTUA~1\R-TENS~1\Lib\site-packages\keras\src\losses.py", line 2432, in binary_crossentropy
backend.binary_crossentropy(y_true, y_pred, from_logits=from_logits),
File "C:\Users\CSC\DOCUME~1\VIRTUA~1\R-TENS~1\Lib\site-packages\keras\src\backend.py", line 5809, in binary_crossentropy
return tf.nn.sigmoid_cross_entropy_with_logits(
ValueError: `logits` and `labels` must have the same shape, received ((None, 16) vs (None, 1)).
Run ]8;;rstudio:run:reticulate::py_last_error()`reticulate::py_last_error()`]8;; for details.prediction = model %>% predict(as.matrix(test_dtm))
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204/204 [==============================] - 1s 3ms/step
204/204 [==============================] - 1s 3ms/steppred_labels = ifelse(prediction > 0.5,1,0)
# Creating a cross table to compare the predicted labels to the true labels
movies_table = table(pred_labels, test_labels)Error in table(pred_labels, test_labels) :
all arguments must have the same lengthThe precision for Naive Bayes was approx 0.3 And the recall was approx 0.55